{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akars\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure ED1\n",
    "import sys\n",
    "sys.modules[__name__].__dict__.clear()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "\n",
    "basin=['GLOBAL','NCA','EAS','SWA','OCE','EAF',]\n",
    "ti=['A) GLOBAL','B) NCA','C) EAS','D) SWA','E) OCE','F) EAF',]\n",
    "#fig, ax = plt.subplots(6,1)\n",
    "fig, ax = plt.subplots(6,1, figsize=(9, 11),sharex=True)\n",
    "fig.tight_layout(pad=2.0)\n",
    "bottom = np.zeros(41)\n",
    "j=-1\n",
    "for i in basin:\n",
    "    \n",
    "    file = pd.read_excel('DATA/FIG_ED1/TC_Stats_Land_Basin.xlsx',sheet_name=i) \n",
    "    year = np.array(file.loc[:,\"Year\"])\n",
    "    file\n",
    "    \n",
    "    TCs = {\n",
    "    \"TS\": np.array(file.loc[:,\"TS\"]),\n",
    "    \"Cat 1\":np.array(file.loc[:,\"Cat 1\"]),\n",
    "    \"Cat 2\":np.array(file.loc[:,\"Cat 2\"]),\n",
    "    \"Cat 3\":np.array(file.loc[:,\"Cat 3\"]),\n",
    "    \"Cat 4\":np.array(file.loc[:,\"Cat 4\"]),\n",
    "    \"Cat 5\":np.array(file.loc[:,\"Cat 5\"]),   \n",
    "           }\n",
    "    width = 10\n",
    "    bottom=0\n",
    "    j=j+1\n",
    "    for boolean, TC in TCs.items():\n",
    "            \n",
    "            ax[j].bar(year, TC,label=boolean, bottom=bottom)\n",
    "            bottom += TC\n",
    "            \n",
    "            ax[j].set_title(ti[j],loc='left')\n",
    "            \n",
    "            ax[j].set_ylabel('Count')\n",
    "\n",
    "#plt.show()\n",
    "ax[5].legend(loc=9,ncol=6,bbox_to_anchor=(0.62, 1.3)) \n",
    "ax[j].set_xlabel('Year')\n",
    "plt.savefig('FIG_EXT/Fig_ED1.png', dpi=300)\n",
    "             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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